DocumentCode :
549214
Title :
Tracking multiple extended objects — A Markov chain Monte Carlo approach
Author :
Richter, Eric ; Obst, Marcus ; Noll, Michael ; Wanielik, Gerd
Author_Institution :
Dept. of Commun. Eng., Chemnitz Univ. of Technol., Chemnitz, Germany
fYear :
2011
fDate :
5-8 July 2011
Firstpage :
1
Lastpage :
8
Abstract :
This paper proposes a multiple object tracking system for spatially extended objects, whose number is a priori not known and dynamically changing over time. Compared to the expected size of the objects, a high resolution range measuring sensor is used within an implementation of the proposed system. For that, the Bayesian framework is rigorously utilized and implemented using a reversible jump Markov chain Monte Carlo sampling approach. A priori knowledge like object dynamics is statistically expressed and integrated into one Bayes filter. This includes how objects lookalike and move, where they are expected to appear & disappear, and how they do interact with each other. The functionality of the system is shown in simulative results.
Keywords :
Bayes methods; Markov processes; Monte Carlo methods; filtering theory; object tracking; sensors; Bayes filter; Bayesian framework; high resolution range measuring sensor; multiple extended object tracking system; reversible jump Markov chain Monte Carlo sampling approach; Equations; Markov processes; Mathematical model; Monte Carlo methods; Proposals; Spatial resolution; Tracking; Markov chain Monte Carlo; Spatially extended object tracking; data association;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Fusion (FUSION), 2011 Proceedings of the 14th International Conference on
Conference_Location :
Chicago, IL
Print_ISBN :
978-1-4577-0267-9
Type :
conf
Filename :
5977657
Link To Document :
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